Overview

• 📊 Modern vector index benchmark with embedding datasets
• 🎯 Includes datasets for both in-distribution and out-out-distribution settings
• 🏆 Includes the most comprehensive collection of state-of-the-art vector search algorithms
• 💎 Support for quantized datasets in both 8-bit integer and binary precision
• 🖥️ Support for HPC environments with Slurm and NUMA
• 🚀 Support for GPU algorithms

Results

The current VIBE results can be viewed on our website:

https://vector-index-bench.github.io

The website also features several other tools and visualizations to explore the results, and the underlying result files are also available in the website repository.

The results are run on Intel Xeon Gold 6230 (Cascade Lake) CPUs with support for AVX-512 instructions. All algorithms are benchmarked using a single core. The GPU algorithms are run using an NVIDIA V100 (32 GB). The next results update will use AMD Turin 9965 CPUs, while GPU algorithms will be run using NVIDIA GH200 (96 GB).

Publication

E. Jääsaari, V. Hyvönen, M. Ceccarello, T. Roos, M. Aumüller. VIBE: Vector Index Benchmark for Embeddings. Journal of Data-centric Machine Learning Research, 2026.

Authors

VIBE is maintained by Elias Jääsaari, Matteo Ceccarello, and Martin Aumüller.

Alternative Benchmarks

Please check out big-ann-benchmarks (NeurIPS 2021/2023) for the state-of-the-art in billion-scale ANN and constrained ANN, such as ANN under filtered or sparse workloads.

Credits

The evaluation code and some algorithm implementations in VIBE are based on the ann-benchmarks project.

License

VIBE is available under the MIT License (see LICENSE). The pyyaml library is also distributed in the vibe folder under the MIT License.

Getting started

Requirements

• Linux
• Apptainer (or Singularity)
• Python 3.6 or newer

For example, to install Apptainer on Ubuntu:

sudo add-apt-repository -y ppa:apptainer/ppa
sudo apt update
sudo apt install -y apptainer

Some algorithms may require that the CPU supports AVX-512 instructions and some algorithms may require an Intel CPU due to a dependency on Intel MKL. The GPU algorithms assume that an NVIDIA GPU is available.

[!IMPORTANT] For accurate benchmarking, it is recommended to disable SMT/hyperthreading:

echo off | sudo tee /sys/devices/system/cpu/smt/control

On hybrid architectures (e.g., Intel Raptor Lake), it is recommended to disable efficiency (E) cores.

If not running in an HPC or cloud environment, it is also recommended to set the performance governor

sudo cpupower frequency-set -g performance

and to check that transparent huge pages are set to madvise or never:

cat /sys/kernel/mm/transparent_hugepage/enabled

Building library images

Building all library images can be done using

./install.sh

Use ./install.sh --skip-gpu if you don't need GPU methods. To build an image for a single library:

./install.sh --algorithm hnswlib

[!TIP] install.sh takes an argument --build-dir that specifies the temporary build directory. For example, to speed up the build in a cluster environment, you can set the build directory to a location on an SSD while the project files are on a slower storage medium.

[!TIP] See an example Slurm job for building the libraries using Slurm.

Running benchmarks

The benchmarks for a single dataset can be run using run.py. For example:

python3 run.py --dataset agnews-mxbai-1024-euclidean

The run.py script does not depend on any external libraries and can therefore be used without a container or a virtual environment.

Common options for run.py:

• --parallelism n: Use n processes for benchmarking.
• --module mod: Run the benchmark only for algorithms in module (library) mod.
• --algorithm algo: Run the benchmark for only algorithm algo.
• --count k: Run the benchmarks using k nearest neighbors (default 100).
• --gpu: Run the benchmark in GPU mode.

For all options, see

python3 run.py --help

The benchmark should take less than 24 hours to run for a given dataset using parallelism > 12. We recommend having at least 16 GB of memory per used core.

[!TIP] See an example Slurm job for running the benchmark using Slurm.

Plotting results

You should first build the plot.sif image:

singularity build plot.sif plot.def

Before plotting, the current results must first be exported:

./export_results.sh --parallelism 8

The results for a dataset can then plotted with e.g.:

./plot.sh --dataset agnews-mxbai-1024-euclidean

To plot the radar chart above, use:

./plot.sh --plot-type radar

For all available options, see:

./plot.sh --help

[!TIP] You can also use uv to directly run export_results.py and plot.py without building the container image if preferable. The arguments for these scripts are the same as above.

Creating datasets from scratch

The benchmark code downloads precomputed embedding datasets. However, the datasets can also be recreated from scratch, and it is also possible to create new datasets by modifying the datasets.py file.

Creating the datasets can be done using create_dataset.sh. It first requires that dataset.sif is built:

singularity build dataset.sif dataset.def

The VIBE_CACHE environment variable should be set to a cache directory with at least 200 GB of free space when creating image embeddings using the Landmark or ImageNet datasets. Datasets can then be created using the --dataset argument (the --nv argument specifies that an available GPU can be used):

export VIBE_CACHE=$LOCAL_SCRATCH
./create_dataset.sh --singularity-args "--bind $LOCAL_SCRATCH:$LOCAL_SCRATCH --nv" --dataset agnews-mxbai-1024-euclidean

[!TIP] See an example Slurm job for creating datasets using Slurm.

Adding a new method to the benchmark

VIBE is an on-going effort and we actively welcome new additions to the benchmarks.

See the new method guide. In short, add your algorithm in the folder vibe/algorithms/{METHOD}/ by providing:

• Python wrapper in module.py
• Singularity container definition in image.def
• Hyperparameter grid in config.yml

Please refer to e.g. the hnswlib module for a reference implementation.

Adding a new dataset to the benchmark

See the new dataset guide for instructions on creating a dataset.

Running the website locally

The results website can be run locally by following the instructions in the website repository.

Evaluation

In-distribution datasets

Out-of-distribution datasets

Deprecated datasets

Deprecated datasets will remain available, but their benchmark results will not be updated in the future.

Algorithms